electricsheepafrica/africa-ilo-luu-xlu2-sex-geo-mts-rt-combined-rate-of-time-related-underemployment-and

Combined rate of time-related underemployment and unemployment (LU2) by sex, rural / urban | Africa (ILOSTAT)

🌍 3,511 observations · 32 Africa countries · 1996–2025 · Repackaged by Electric Sheep Africa

TL;DR

This dataset contains 3,511 observations of Other measures of labour underutilization data across 32 Africa countries, spanning 1996–2025, covering 1 distinct indicators.

About the source

ILOSTAT is the ILO's central statistics database, the leading global source for labour statistics. It compiles indicators across employment, unemployment, wages, working time, child labour, informal economy, social protection, occupational injuries, and SDG decent work targets — drawing on national labour force surveys, household income surveys, establishment surveys, and administrative records. Coverage spans 200+ economies, with the ILO's Department of Statistics responsible for harmonisation.

• Source: ILOSTAT
• Publisher: International Labour Organization (ILO)
• License: cc-by-4.0
• Topic: Other measures of labour underutilization

Methodology

Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=LUU_XLU2_SEX_GEO_MTS_RT and filtered to Africa ISO3 country codes. ILOSTAT harmonises raw survey microdata using ICLS (International Conference of Labour Statisticians) definitions; sources are flagged in the source.label column for traceability.

Geographic coverage

32 Africa countries · top rows shown below, sorted by row count:

Country	Rows	First year	Last year
ZAF	468	2008	2024
RWA	270	2014	2025
ZMB	216	2017	2024
ZWE	208	2011	2024
GHA	207	2006	2024
AGO	194	2019	2025
UGA	190	2010	2021
SEN	189	2011	2024
EGY	164	2016	2024
MLI	162	2018	2024
NGA	113	2019	2024
GMB	90	2012	2025
SWZ	85	2016	2023
KEN	81	2019	2022
CIV	81	2016	2019
...	17 more countries

Indicators (sample)

• LUU_XLU2_SEX_GEO_MTS_RT — Combined rate of time-related underemployment and unemployment (LU2) by sex, rural / urban area and marital status (%)

Schema

Column	Type	Description	Example
ref_area	string	ISO 3166-1 alpha-3 country code	AGO
ref_area.label	string	Country name in English	Angola
source	string	ILOSTAT source code (e.g. labour force survey)	BA:13951
source.label	string	Source name in English	LFS - Employment Survey
indicator	string	ILOSTAT indicator code	LUU_XLU2_SEX_GEO_MTS_RT
indicator.label	string	Indicator name in English	Combined rate of time-related underem…
sex	string	Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female)	SEX_T
sex.label	string	—	Total
classif1	string	First classification variable (age, education, status, etc.)	GEO_COV_NAT
classif1.label	string	—	Area type: National
classif2	string	Second classification variable where applicable	MTS_AGGREGATE_TOTAL
classif2.label	string	—	Marital status (Aggregate): Total
time	int64	Observation year	2025
obs_value	float64	Observed indicator value (unit varies — see indicator definition)	11.141
obs_status	string	Observation status flag (e.g. provisional, unreliable)	U
obs_status.label	string	—	Unreliable
note_indicator	string	—	I11:264
note_indicator.label	string	—	Break in series: Methodology revised
note_source	string	—	R1:3513
note_source.label	string	—	Repository: ILO-STATISTICS - Micro da…

Disaggregation dimensions

The following columns provide disaggregation dimensions:

• sex (3 unique values): SEX_T, SEX_M, SEX_F

Data quality & caveats

• Data is annual frequency. Some indicators also publish monthly or quarterly series — those are not included here.
• When an indicator has multiple sources for the same country×year, the ILO-selected 'best source' is used.
• Disaggregation columns (sex, classif1, classif2) are non-null only when the indicator publishes that breakdown.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-ilo-luu-xlu2-sex-geo-mts-rt-combined-rate-of-time-related-underemployment-and")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

kenya = df[df["ref_area"] == "KEN"]

Time-series for a single indicator

sample = (df[df["indicator"] == "LUU_XLU2_SEX_GEO_MTS_RT"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="LUU_XLU2_SEX_GEO_MTS_RT")

Pivot to country × year matrix

matrix = (df[df["indicator"] == "LUU_XLU2_SEX_GEO_MTS_RT"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

@misc{africa_ilo_luu_xlu2_sex_geo_mts_rt_combined_rate_of_time_related_underemployment_and_2025,
  title        = {Combined rate of time-related underemployment and unemployment (LU2) by sex, rural / urban | Africa (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_GEO_MTS_RT},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-ilo-luu-xlu2-sex-geo-mts-rt-combined-rate-of-time-related-underemployment-and}}
}

License

Released under cc-by-4.0.

Original data © International Labour Organization (ILO). When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.

About Electric Sheep

Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepafrica

---

Provenance: ingested 2026-05-26 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=LUU_XLU2_SEX_GEO_MTS_RT